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A Convolutional Neural Network architecture was used to classify variousisotopes of time-sequenced gamma-ray spectra, a typical output of a radiationdetection system of a type commonly fielded for security or environmentalmeasurement purposes. A two-dimensional surface (waterfall plot) in time-energyspace is interpreted as a monochromatic image and standard image-based CNNtechniques are applied. This allows for the time-sequenced aspects of featuresin the data to be discovered by the network, as opposed to standard algorithmswhich arbitrarily time bin the data to satisfy the intuition of a humanspectroscopist. The CNN architecture and results are presented along with acomparison to conventional techniques. The results of this novel application ofimage processing techniques to radiation data will be presented along with acomparison to more conventional adaptive methods.

An Application of CNNs to Time Sequenced One Dimensional Data in Radiation Detection